• Written by Azhar Aliyar, Director

A monthly financial report sits on the desk of a chief executive, its pages filled with numbers and charts that tell a story of the organisation's performance. But what if the story could be told in a more dynamic way, with the ability to ask follow-up questions and receive answers that drive action? This is where agentic AI workflows for executive leadership come in, enabling systems to take multi-step action rather than just answering a single question.

What separates an agent from a chatbot

An agent is more than just a chatbot that can answer questions. It is a system that can use tools, maintain state, and work towards a goal. This means it can interact with other systems, store information, and make decisions based on that information. In the context of executive leadership, an agent can use financial systems, operational data, and sales performance metrics to inform its decisions. A chatbot, on the other hand, is limited to providing answers to specific questions, without the ability to take action or maintain context.

The key difference between an agent and a chatbot is the ability to take action. An agent can initiate workflows, send notifications, and make decisions, whereas a chatbot is limited to providing information. This makes an agent a much more powerful tool for executive leadership, as it can help drive decision-making and action.

Where a human approval step belongs

One of the key considerations when implementing agentic AI workflows for executive leadership is where to include human approval steps. While it may be tempting to remove human approval steps altogether, this is usually premature. Human approval is still necessary to ensure that decisions are made with the appropriate level of oversight and judgement. However, the approval step should be placed at the point where the agent is about to take action, rather than at the point where it is gathering information.

For example, an agent may gather financial data and operational metrics to inform a decision about resource allocation. In this case, the human approval step should come after the agent has made its recommendation, but before it takes action. This ensures that the decision is made with the appropriate level of oversight and judgement.

Executive leadership data

Agentic AI workflows for executive leadership rely on a range of data sources, including consolidated financials, operational KPIs, sales performance, and headcount. This data is used to inform decisions and drive action, and is typically gathered from a range of systems, including financial systems, operational databases, and sales platforms. The key is to have a single, unified view of the organisation's performance, which can be used to inform decision-making.

For example, a chief executive may use an agent to gather data on sales performance, operational metrics, and financials, in order to inform a decision about resource allocation. The agent can use this data to make recommendations, which can then be approved or rejected by the chief executive. To learn more about how agentic AI workflows can support executive and strategic leadership, please visit our website.

What it changes

Agentic AI workflows for executive leadership can change the way that organisations approach decision-making and action. One of the key benefits is the ability to answer cross-functional questions that nobody can answer in the room. For example, an agent can gather data from financial systems, operational databases, and sales platforms to answer questions about the impact of a particular decision on the organisation as a whole.

Another key benefit is the ability to provide a single, unified view of the organisation's performance. This can help to resolve conflicts between different functions, each of which may have its own version of a particular figure. By providing a single, authoritative source of truth, an agent can help to drive decision-making and action.

Measured with

Agentic AI workflows for executive leadership can be measured in a range of ways, including decision latency, actions of the form "come back with numbers", and definition conflicts resolved. Decision latency refers to the time it takes to make a decision, and can be reduced by using an agent to gather data and inform decision-making. Actions of the form "come back with numbers" refer to the ability of an agent to provide detailed, data-driven answers to questions, which can help to inform decision-making.

Definition conflicts resolved refers to the ability of an agent to provide a single, unified view of the organisation's performance, which can help to resolve conflicts between different functions. By measuring these metrics, organisations can get a sense of the impact that agentic AI workflows are having on their decision-making and action.

What this does not do

Agentic AI workflows for executive leadership are not a replacement for human judgement and oversight. While an agent can gather data and inform decision-making, it is still necessary to have human approval steps in place to ensure that decisions are made with the appropriate level of oversight and judgement. Additionally, agentic AI workflows are not suitable for all types of decisions, and are typically best used for decisions that are data-driven and repeatable.

If you are interested in learning more about how agentic AI workflows can support your organisation, please contact us to discuss your needs and how we can help. We can work with you to design and implement agentic AI workflows that meet your specific requirements and help drive decision-making and action.